With the advances in embedded microcontrollers, inexpensive miniature sensors, and wireless networking technologies, there has been a growing interest in using wireless sensor networks in medical applications. For example, wireless sensor networks can replace expensive and cumbersome wired devices for pre-hospital and ambulatory emergency care when real-time and continuous monitoring of vital signs is needed. Moreover, body sensor networks can be formed by placing low-power wireless devices on or around the body, enabling long-term monitoring of physiological data.
Personal Emergency Response Systems (PERS) are provided, where a user can use a button (PHB—Personal Help Button) to call for assistance. After the button has been pressed, a wireless telephone connection takes care that the help center of the PERS service operator can assist the user. Recently, a fall detector, i.e. wireless sensor which may include an accelerometer, has been added to the PHB, so that calls for help can be made without the need for an explicit button press.
Furthermore, for elderly patients and people with chronic diseases, an in-house wireless sensor network allows convenient collection of medical data while they are staying at home, thus reducing the burden of hospital stay. The collected data can be passed onto the Internet through a PDA, a cell-phone, or a home computer. The care givers thus have remote access to the patient's health status, facilitating long-term rehabilitation and early detection of certain physical diseases. If there are abnormal changes in the patient status, caregivers can be notified in a timely manner, and immediate treatment can be provided.
Vitals signs like respiration rate and heart rate can be monitored by a new generation of sensors which use wireless connectivity and make use of novel sensing principles. An example of a novel sensing principle is the use of inertial sensors (such as accelerometers, for example) to sense respiration rate, heart rate or other vital signs. In general, inertial measurement components sense either translational acceleration or angular rate. The advances in micro-electromechanical systems (MEMS) and other micro-fabrication techniques have greatly reduced the cost and the size of these devices, and they can be easily embedded into wireless and mobile platforms. Gyroscopes and accelerometers are two common inertial sensors that can be used to capture human motion continuously. The wireless connectivity provides more comfort to the patient and simplifies the operational usage. The sensor can be attached below the clothing of the patient, for patient convenience. However, this makes it cumbersome for the physician to operate the sensor: physically, to find the sensor and knob, but socially, to reach below the clothes. What's more, for hygienic reasons, the sensors are preferably completely sealed and free of knobs. This poses the problem of user control. Using the wireless connection may solve, but leaves the problem of initiating the connection. Power consumption constraints prohibit the radio to be switched on continuously to scan for potential commands.
The use of inertial sensors, such as accelerometers, for detection and classification of human gestures introduces the problem of the reliable distinction between user control commands (gestures) and other motions (movements by the patient as they occur in daily life). For example, in Application Note AN2768: “LIS331 DL 3-axis digital MEMS accelerometer: translates finger taps into actions” by ST, June 2008, a tap detection procedure is described. The procedure is based on sensing the acceleration and identifying a tap when the signal surpasses a certain threshold, while returning below the threshold within a prescribed time window. In a similar way, double taps are detected, by observing a pair of threshold crossings within a prescribed period where each crossing is of a prescribed duration. Although threshold crossing and timing are essential features for detecting a tap, they are not sufficient to obtain reliable detection, in the sense of a low rate of false positives (non-tapping movements that induce a similar signal that will pass the detection procedure) acceptable for practical use. For example, upon heal strike during walking the acceleration signals can show peaks of short duration, and, hence, can trigger the detection of a “tap”.